Numpy arange()

Numpy

numpy.arange(start,stop, step,dtype=None)
Return ndarray by using start, stop, step and dtype.
startnumber, Optional,default is 0, Start value of the elements
stopnumber, Optional,Upper limit of start ( value not included ).
stepnumber, Optional, default is 1
dtypetype of output, Optional, default is dtype of input arguments

Examples

Creating array ( start = 0 )
import numpy as np
print(np.arange(0)) # []
print(np.arange(5)) # [0 1 2 3 4]
With start stop and step options
print(np.arange(start=1,stop=5,step=2)) # [1 3]
print(np.arange(1,5))
Output
[1 2 3 4]
Creating empty array
x=np.arange(3,3)
print(x) # []

Using start stop and step

Sample codes below are with output.
print(np.arange(start=1,stop=5,step=2)) # [1 3]
print(np.arange(1,5,2)) # [1 3]

Using negative start, strop and steps

print(np.arange(-10,-3,2)) # [-10  -8  -6  -4]
print(np.arange(-3,-10,-2)) # [-3 -5 -7 -9]
print(np.arange(-3,10,3)) # [-3  0  3  6  9]
print(np.arange(.4, 2.8,.7)) # [0.4 1.1 1.8 2.5]
print(np.arange(-2.8, 2,.9)) # [-2.8 -1.9 -1.  -0.1  0.8  1.7]

dtype

We can use option dtype ( dtype=np.int32 )
print(np.arange(1,5,dtype=np.int8)) # [1 2 3 4]
print(np.arange(1,5,dtype=np.float64)) # [1. 2. 3. 4.]
Getting the dtype
my_ar=np.arange(1,5,dtype=np.float64)
print(my_ar.dtype) # float64
Numpy eye() ones() bincount() linspace()


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